code: def conv_tr(
      in_planes,
      out_planes,
      kernel_size,
      upsample_stride=1,
      dilation=1,
      bias=False,
      conv_type=ConvType.HYPERCUBE,
      D=-1,
  ):
      assert D > 0, "Dimension must be a positive integer"
      region_type, axis_types, kernel_size = convert_conv_type(
          conv_type, kernel_size, D
      )
      kernel_generator = ME.KernelGenerator(
          kernel_size,
          upsample_stride,
          dilation,
          region_type=region_type,
          axis_types=axis_types,
          dimension=D,
      )
  
      return ME.MinkowskiConvolutionTranspose(
          in_channels=in_planes,
          out_channels=out_planes,
          kernel_size=kernel_size,
          stride=upsample_stride,
          dilation=dilation,
          bias=bias,
          kernel_generator=kernel_generator,
          dimension=D,
      )
  
file_path: torch-points3d/torch_points3d/modules/MinkowskiEngine/common.py
imports: import collections
  from enum import Enum
  import torch.nn
  import MinkowskiEngine
label_desc: Use image node_id 5 for calling a global function with example usage: conv_tr(in_planes, out_planes, kernel_size, upsample_stride, dilation, bias, conv_type, D) and returns: ME